



'''simple docstring'''

import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


SCREAMING_SNAKE_CASE__     :						List[str]	=  '''bert-base-cased'''
SCREAMING_SNAKE_CASE__     :						str	=  '''google/pegasus-xsum'''
SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
SCREAMING_SNAKE_CASE__     :						List[str]	=  '''patrickvonplaten/t5-tiny-random'''
SCREAMING_SNAKE_CASE__     :						Dict	=  '''sshleifer/bart-tiny-random'''
SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  '''sshleifer/tiny-mbart'''
SCREAMING_SNAKE_CASE__     :						int	=  '''sshleifer/tiny-marian-en-de'''






def 				a  (     UpperCamelCase_    :    Path      ,					UpperCamelCase_    :    list			)       ->	Dict:
		snake_case__	     ='\n'.join(UpperCamelCase_			)
		Path(UpperCamelCase_			).open('w'			).writelines(UpperCamelCase_			)






def 				a  (     UpperCamelCase_    :    List[Any]			)       ->	List[Any]:
		for split in ["train", "val", "test"]:
				_dump_articles(os.path.join(UpperCamelCase_      ,					f"""{split}.source"""			)      ,					UpperCamelCase_			)
				_dump_articles(os.path.join(UpperCamelCase_      ,					f"""{split}.target"""			)      ,					UpperCamelCase_			)
		return tmp_dir


class 							a__( snake_case__     ):






			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]					,   )
			@slow
			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Any:
					snake_case__	     =AutoTokenizer.from_pretrained(_UpperCAmelCase						)
					snake_case__	     =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
					snake_case__	     =max(len(tokenizer.encode(_UpperCAmelCase						)						) for a in ARTICLES						)
					snake_case__	     =max(len(tokenizer.encode(_UpperCAmelCase						)						) for a in SUMMARIES						)
					snake_case__	     =4
					snake_case__	     =8
					assert max_len_target > max_src_len  # Will be truncated
					assert max_len_source > max_src_len  # Will be truncated
					snake_case__     ,	snake_case__	     ='ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
					snake_case__	     =SeqaSeqDataset(
					    _UpperCAmelCase					,   data_dir=_UpperCAmelCase					,   type_path='train'					,   max_source_length=_UpperCAmelCase					,   max_target_length=_UpperCAmelCase					,   src_lang=_UpperCAmelCase					,   tgt_lang=_UpperCAmelCase					,   )
					snake_case__	     =DataLoader(_UpperCAmelCase					,   batch_size=2					,   collate_fn=train_dataset.collate_fn						)
					for batch in dataloader:
							assert isinstance(_UpperCAmelCase					,   _UpperCAmelCase						)
							assert batch["attention_mask"].shape == batch["input_ids"].shape
							# show that articles were trimmed.
							assert batch["input_ids"].shape[1] == max_src_len
							# show that targets are the same len
							assert batch["labels"].shape[1] == max_tgt_len
							if tok_name != MBART_TINY:
									continue
							# check language codes in correct place
							snake_case__	     =shift_tokens_right(batch['labels']					,   tokenizer.pad_token_id						)
							assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
							assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
							assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
							assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

							break  # No need to test every batch






			@parameterized.expand([BART_TINY, BERT_BASE_CASED]						)
			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Any:
					snake_case__	     =AutoTokenizer.from_pretrained(_UpperCAmelCase						)
					snake_case__	     =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
					snake_case__	     =max(len(tokenizer.encode(_UpperCAmelCase						)						) for a in ARTICLES						)
					snake_case__	     =max(len(tokenizer.encode(_UpperCAmelCase						)						) for a in SUMMARIES						)
					snake_case__	     =4
					snake_case__	     =LegacySeqaSeqDataset(
					    _UpperCAmelCase					,   data_dir=_UpperCAmelCase					,   type_path='train'					,   max_source_length=20					,   max_target_length=_UpperCAmelCase					,   )
					snake_case__	     =DataLoader(_UpperCAmelCase					,   batch_size=2					,   collate_fn=train_dataset.collate_fn						)
					for batch in dataloader:
							assert batch["attention_mask"].shape == batch["input_ids"].shape
							# show that articles were trimmed.
							assert batch["input_ids"].shape[1] == max_len_source
							assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
							# show that targets were truncated
							assert batch["labels"].shape[1] == trunc_target  # Truncated
							assert max_len_target > trunc_target  # Truncated
							break  # No need to test every batch






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'						)

					snake_case__	     =Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)						)
					snake_case__	     =tmp_dir.joinpath('train.source'						).open().readlines()
					snake_case__	     =Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)						)
					pack_data_dir(_UpperCAmelCase					,   _UpperCAmelCase					,   128					,   _UpperCAmelCase						)
					snake_case__	     ={x.name for x in tmp_dir.iterdir()}
					snake_case__	     ={x.name for x in save_dir.iterdir()}
					snake_case__	     =save_dir.joinpath('train.source'						).open().readlines()
					# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
					# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
					assert len(_UpperCAmelCase						) < len(_UpperCAmelCase						)
					assert len(_UpperCAmelCase						) == 1
					assert len(packed_examples[0]						) == sum(len(_UpperCAmelCase						) for x in orig_examples						)
					assert orig_paths == new_paths






			@pytest.mark.skipif(not FAIRSEQ_AVAILABLE					,   reason='This test requires fairseq'						)
			def 						_lowercase  (     self						)							->							Optional[int]:
					if not FAIRSEQ_AVAILABLE:
							return
					snake_case__     ,	snake_case__     ,	snake_case__	     =self._get_dataset(max_len=64						)
					snake_case__	     =64
					snake_case__	     =ds.make_dynamic_sampler(_UpperCAmelCase					,   required_batch_size_multiple=_UpperCAmelCase						)
					snake_case__	     =[len(_UpperCAmelCase						) for x in batch_sampler]
					assert len(set(_UpperCAmelCase						)						) > 1  # it's not dynamic batch size if every batch is the same length
					assert sum(_UpperCAmelCase						) == len(_UpperCAmelCase						)  # no dropped or added examples
					snake_case__	     =DataLoader(_UpperCAmelCase					,   batch_sampler=_UpperCAmelCase					,   collate_fn=ds.collate_fn					,   num_workers=2						)
					snake_case__	     =[]
					snake_case__	     =[]
					for batch in data_loader:
							snake_case__	     =batch['input_ids'].shape
							snake_case__	     =src_shape[0]
							assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
							snake_case__	     =np.product(batch['input_ids'].shape						)
							num_src_per_batch.append(_UpperCAmelCase						)
							if num_src_tokens > (max_tokens * 1.1):
									failures.append(_UpperCAmelCase						)
					assert num_src_per_batch[0] == max(_UpperCAmelCase						)
					if failures:
							raise AssertionError(f"""too many tokens in {len(_UpperCAmelCase						)} batches"""						)






			def 						_lowercase  (     self						)							->							int:
					snake_case__     ,	snake_case__     ,	snake_case__	     =self._get_dataset(max_len=512						)
					snake_case__	     =2
					snake_case__	     =ds.make_sortish_sampler(_UpperCAmelCase					,   shuffle=_UpperCAmelCase						)

					snake_case__	     =DataLoader(_UpperCAmelCase					,   batch_size=_UpperCAmelCase					,   collate_fn=ds.collate_fn					,   num_workers=2						)
					snake_case__	     =DataLoader(_UpperCAmelCase					,   batch_size=_UpperCAmelCase					,   collate_fn=ds.collate_fn					,   num_workers=2					,   sampler=_UpperCAmelCase						)

					snake_case__	     =tokenizer.pad_token_id

					def count_pad_tokens(_UpperCAmelCase					,   _UpperCAmelCase="input_ids"						):
							return [batch[k].eq(_UpperCAmelCase						).sum().item() for batch in data_loader]

					assert sum(count_pad_tokens(_UpperCAmelCase					,   k='labels'						)						) < sum(count_pad_tokens(_UpperCAmelCase					,   k='labels'						)						)
					assert sum(count_pad_tokens(_UpperCAmelCase						)						) < sum(count_pad_tokens(_UpperCAmelCase						)						)
					assert len(_UpperCAmelCase						) == len(_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase=1000					,   _UpperCAmelCase=128						)							->							Dict:
					if os.getenv('USE_REAL_DATA'					,   _UpperCAmelCase						):
							snake_case__	     ='examples/seq2seq/wmt_en_ro'
							snake_case__	     =max_len * 2 * 64
							if not Path(_UpperCAmelCase						).joinpath('train.len'						).exists():
									save_len_file(_UpperCAmelCase					,   _UpperCAmelCase						)
					else:
							snake_case__	     ='examples/seq2seq/test_data/wmt_en_ro'
							snake_case__	     =max_len * 4
							save_len_file(_UpperCAmelCase					,   _UpperCAmelCase						)

					snake_case__	     =AutoTokenizer.from_pretrained(_UpperCAmelCase						)
					snake_case__	     =SeqaSeqDataset(
					    _UpperCAmelCase					,   data_dir=_UpperCAmelCase					,   type_path='train'					,   max_source_length=_UpperCAmelCase					,   max_target_length=_UpperCAmelCase					,   n_obs=_UpperCAmelCase					,   )
					return ds, max_tokens, tokenizer






			def 						_lowercase  (     self						)							->							Optional[int]:
					snake_case__     ,	snake_case__     ,	snake_case__	     =self._get_dataset()
					snake_case__	     =set(DistributedSortishSampler(_UpperCAmelCase					,   256					,   num_replicas=2					,   rank=0					,   add_extra_examples=_UpperCAmelCase						)						)
					snake_case__	     =set(DistributedSortishSampler(_UpperCAmelCase					,   256					,   num_replicas=2					,   rank=1					,   add_extra_examples=_UpperCAmelCase						)						)
					assert idsa.intersection(_UpperCAmelCase						) == set()






			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]					,   )
			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Optional[int]:
					snake_case__	     =AutoTokenizer.from_pretrained(_UpperCAmelCase					,   use_fast=_UpperCAmelCase						)
					if tok_name == MBART_TINY:
							snake_case__	     =SeqaSeqDataset(
							    _UpperCAmelCase					,   data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)					,   type_path='train'					,   max_source_length=4					,   max_target_length=8					,   src_lang='EN'					,   tgt_lang='FR'					,   )
							snake_case__	     =train_dataset.dataset_kwargs
							assert "src_lang" in kwargs and "tgt_lang" in kwargs
					else:
							snake_case__	     =SeqaSeqDataset(
							    _UpperCAmelCase					,   data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)					,   type_path='train'					,   max_source_length=4					,   max_target_length=8					,   )
							snake_case__	     =train_dataset.dataset_kwargs
							assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
							assert len(_UpperCAmelCase						) == 1 if tok_name == BART_TINY else len(_UpperCAmelCase						) == 0









'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def 				a  (     UpperCamelCase_    :    Any			)       ->	List[str]:
		snake_case__	     =os.path.join(args.tf_model_dir      ,					'parameters.json'			)
		snake_case__	     =json.loads(open(UpperCamelCase_			).read()			)
		if not params:
				raise ValueError(
				    f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""			)
		if not args.output.endswith('.pt'			):
				snake_case__	     =args.output + '.pt'
		snake_case__	     =OrderedDict()
		with tf.device('/CPU:0'			):
				snake_case__	     =tf.train.load_checkpoint(args.tf_model_dir			)
				snake_case__	     =reader.get_variable_to_shape_map()
				for key_name in shapes.keys():
						snake_case__	     =reader.get_tensor(UpperCamelCase_			).astype(np.floataa			)
						if key_name.endswith('/adam_m'			) or key_name.endswith('/adam_v'			):
								continue
						if key_name.startswith('pasts/'			):
								if key_name.startswith('pasts/mlp'			):
										snake_case__	     =int(key_name[9]			)
								elif key_name.startswith('pasts/out'			):
										snake_case__	     =8
								snake_case__	     ='model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
								snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/moe'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/switch_gating/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/softmlp/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/wo/kernel'			) or key_name.endswith('/wi/kernel'			):
										snake_case__	     =key_name[-9:-7]
										for i in range(16			):
												snake_case__	     ='model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
												snake_case__	     =(
												    vnp[i].transpose([1, 0]			).copy()
												)  # In Mesh-Tensorflow, it is one array, so it is divided
												snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/mlp'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/p1/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wi.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p1/bias'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wi.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p2/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wo.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p2/bias'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wo.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/ln'			):
								snake_case__	     =int(key_name[8:].split('/'			)[0]			)
								if key_name.endswith('/b'			):
										snake_case__	     ='model.blocks.%d.feed_forward.norm.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/g'			):
										snake_case__	     ='model.blocks.%d.feed_forward.norm.weight' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/att'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/qkv/kernel'			):
										snake_case__	     =vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
										snake_case__	     =state[:, 0, :, :]
										snake_case__	     =state[:, 1, :, :]
										snake_case__	     =state[:, 2, :, :]
										snake_case__	     =(
										    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =(
										    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =(
										    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/o/kernel'			):
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
										snake_case__	     =(
										    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]			).transpose([1, 0]			).copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/an'			):
								snake_case__	     =int(key_name[8:].split('/'			)[0]			)
								if key_name.endswith('/b'			):
										snake_case__	     ='model.blocks.%d.self_attn.norm.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/g'			):
										snake_case__	     ='model.blocks.%d.self_attn.norm.weight' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif (
						    key_name.startswith('model/wte'			)
						    or key_name.startswith('model/wpe'			)
						    or key_name.startswith('model/ete'			)
						):
								snake_case__	     ={'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
								    key_name[-3:]
								]
								snake_case__	     ='model.%s.weight' % nlayer
								snake_case__	     =vnp.copy()  # same in embedded
								snake_case__	     =torch.tensor(UpperCamelCase_			)
								if key_name.startswith('model/wte'			):
										snake_case__	     ='lm_head.weight'
										snake_case__	     =vnp.copy()  # same in embedded
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/wob'			):
								snake_case__	     ='final_logits_bias'
								snake_case__	     =vnp.copy()  # same in embedded
								snake_case__	     =state.reshape((1, -1)			)
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name == "model/dense/kernel":
								snake_case__	     ='model.last_project.weight'
								snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name == "model/dense_1/bias":
								snake_case__	     ='model.last_project.bias'
								snake_case__	     =vnp.copy()  # same because it is one dimensional
								snake_case__	     =torch.tensor(UpperCamelCase_			)
		torch.save(UpperCamelCase_      ,					args.output			)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__     :						int	=  argparse.ArgumentParser(
			    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
			)
			parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
			parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
			SCREAMING_SNAKE_CASE__     :						str	=  parser.parse_args()
			convert_tf_gptsan_to_pt(args)




